Method and apparatus for adaptively reducing the level of noise in an acquired signal

ABSTRACT

An adaptive filtering method and apparatus for reducing the level of an undesired noise component in an acquired physiological signal having a desired signal component. The acquired physiological signal is applied to one input of the adaptive filter, and a synthetic reference signal that is modeled so as to exhibit a correlation with the desired signal component is applied to another input of the adaptive filter. Thereafter, in a feedback manner, the adaptive filter iteratively adjusts the modeled synthetic reference signal so as to progressively generate a more accurate approximation of the desired signal component in the adaptive filter, which approximation becomes a reconstruction of the acquired physiological signal wherein the level of the undesired noise component is reduced.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and apparatus for adaptivefiltering of a signal having a desired component and an undesiredcomponent, and more specifically, to reducing the level of an undesirednoise component in a physiological signal by adaptive filtering of thephysiological signal using a synthetic reference signal which is modeledto exhibit a correlation with the desired signal.

2. Description of the Prior Art

The measurement of various types of physiological signals is generally adifficult task because the underlying physiological processes thatgenerate physiological signals generate such signals at very lowamplitude levels. Additionally, during acquisition of the physiologicalsignals, the physiological processes, and/or sensors associatedtherewith, typically also generate or become the sources for a noisecomponent that becomes an undesired part of the desired physiologicalsignals.

For example, when electrocardiogram (ECG) signals of a patient aremeasured, sensors detect not only the electrical activity generated bythe electrical depolarization of the heart, a relatively weak signal bythe time it reaches the skin of the patient, but also electricalsignals, artifacts, generated by the activity of other muscles in thepatient. Furthermore, external electrical interference, such as the 60Hz line frequency signals and electrical signals emanating from nearbyelectrical equipment are also acquired as noise components of thedesired physiological signal. Hereinafter, such noise and/or artifactsignals are also referred to as the noise, artifact, or undesiredcomponent of the physiological signal.

Another common physiological signal measurement suffering from lowlevels of desired signal and relatively higher levels of the undesiredcomponent is the measurement of the blood oxygen saturation level of apatient using a pulse oximeter. As well known by those of ordinary skillin the art, a pulse oximeter measures arterial blood oxygen saturationusing a sensor arrangement containing two LED's and a photodiodedetector. The sensor is applied directly to a well perfused part of apatient, such as at a finger or ear. Each LED of the sensor transmitsradiation at a different one of two wavelengths, typically one being redand the other being infrared, to the patient. The photodiode detector isresponsive to the red and infrared light for developing red and infraredelectrical signals that are affected, via transmission or reflection, bythe patient's blood flow in the area between the two LED's and the lightreceiving portion of the photodiode detector. The greater theoxygenation of the blood, the less of the emitted red light is detected,due to greater absorption of the red light by the patient's blood. Inpulse oximeters, the acquired red and infrared signals are processed todevelop a measurement indicative of the current blood oxygenation levelof the patient. Additionally the acquired light signals can be processedfurther to develop a measurement of the pulse rate of the patient,since, as well known, the pulsatile component of the light signalsresults mainly from ventricular contractions of the heart.

Processing of the red and infrared signals for determining bloodoxygenation is based on the Beer-Lambert law, as well known, wherein aratio is generally used to compare the AC and DC components of the redlight (AC_(r) and DC_(r), respectively), to the AC and DC components ofthe infrared light (AC_(ir) and DC_(ir), respectively), in accordancewith the following equation: ##EQU1## The resultant value is applied toan experimentally-determined reference table (look-up-table) to providethe final determination of the measured level of the blood oxygenation.Additionally, as noted above, the AC components of the signals can befurther processed to generate an estimate of the pulse rate.

The blood oxygenation and pulse rate measurements made from opticallyacquired signals are highly prone to inaccuracies due to the undesirednoise and/or artifact components of the acquired signal. The noisecomponents typically result from electrical interference (lights,electro-surgical and other electrical equipment being operated near thepatient), and artifacts typically result from patient movement (causinga relative movement, and concomitant change in light path, between theLED's and detector of the sensor, or even worse, the sudden admission ofroom light into the receiving area of the photodiode detector).Furthermore, the AC component of the acquired signals (which result fromthe pulsatile characteristic of the blood), is very small, typically onthe order of only 1%-5% of the DC value of the acquired signals, as isalso typical of physiological signals. Consequently, such noise andartifacts are extremely detrimental to accurate pulse oximetrymeasurements, leading to the serious problem of an incorrect assessmentof the patient's condition, as well as false alarms to the user of theoximeter.

U.S. Pat. No. 4,955,379 entitled MOTION ARTIFACT REJECTION SYSTEM FORPULSE OXIMETERS, issued Sep. 11, 1990, discloses a band-pass filtering(BPF) technique for removing noise artifacts from pulse oximetrysignals. More specifically, the AC components of each of the acquiredred and infrared signals is initially filtered by a BPF that is broadlytuned to the expected heart rate frequency. The output of the BPF isapplied to a frequency determining circuit, whose output is then used tocause the BPF to track the frequency determined by the frequencydetermining circuit. The theory of this technique is that most of theenergy (and information) in the AC signal is contained at thefundamental frequency, and since the fundamental frequency should be thepulse rate, the frequency determining circuit will determine the pulserate as the fundamental frequency and control the BPF to exclude allother frequencies, along with artifacts. Unfortunately, it is quitepossible that the fundamental frequency determined by the frequencydetermining circuit may in fact be a noise signal, such as one that isgenerated by electrical equipment, causing the oximeter to process thesignal and report erroneous information. Furthermore, even if thefundamental frequency of the pulse rate is correctly determined, sinceother frequency components of the desired pulse signal are excluded, adegraded performance of the oximeter can result. Consequently, thistechnique is undesirable.

U.S. Pat. No. 4,928,692 entitled METHOD AND APPARATUS FOR DETECTINGOPTICAL PULSES, issued May, 29, 1990, discloses a technique wherein theR-wave portion of a patient's ECG waveform is correlated in time withthe optical signals acquired by a pulse oximeter. The correlation isused to develop an enabling signal for processing of the acquiredoptical signals by the oximeter. The theory is that since the pulsatilecomponent of the optical signals contain the information, and theoccurrence of the pulses can be predicted to follow an ECG R-wave by acertain amount, selective timing of oximeter enablement will preventartifact from being admitted into the oximeter and erroneouslyprocessed. Unfortunately, since artifacts can occur at any time, and ingeneral are not in any way correlated so as to have any relation tooccurrence of an ECG R-wave, this technique is also undesirable.

U.S. Pat. No. 5,482,036 entitled SIGNAL PROCESSING APPARATUS AND METHOD,issued Jan. 9, 1996 is representative of a technique that uses anadaptive noise cancellation filter for reducing noise in pulse oximetrysignals acquired using a sensor arrangement having two light sources.FIG. 5 of this prior U.S. Pat. No. 5,482,036, illustrates theapplication of linear adaptive noise cancellation to pulse oximetry. Theacquired signal S.sub.λα comprises two components: a desired signalcomponent Y.sub.λα (a modulation signal that would be obtained from apulse oximeter under ideal conditions), that is additively combined witha noise signal component n.sub.λα. A reference signal n' that has asignificant similarity to the noise component is provided. The objectiveof the cancellation filter is to transform the reference signal n' intoa signal b.sub.λα having as close an approximation of the noisecomponent n.sub.λα as possible. Then, by subtracting the noise componentapproximation from the contaminated signal, a reconstruction Y'.sub.λαof the uncontaminated component of the input signal is obtained.Conversely, a reference signal n' that has a significant similarity tothe desired signal Y'.sub.λα can be provided, and an approximationY'.sub.λα built up for Y.sub.λα using operations on n'. Built up signalY'.sub.λα can then be used as the output of the cancellation filter. Thebasic idea is that if the reference signal contains substantialinformation about only one, not both, of the two input signals, theinput signal S.sub.λα can be separated into some approximation of thedesired signal component Y.sub.λα and some approximation of the noisesignal component n.sub.λα.

However, in pulse-oximetry, a reference signal is not readily available.In the forenoted U.S. Pat. No. 5,482,036 a reference signal is generatedfrom the measured lead signals using a technique based on the fact thatthe desired portions of the acquired red and infrared signals arelinearly related at a given level of blood oxygen saturation. Morespecifically, the acquired red and infrared signals are subtracted fromeach other after determination of an appropriate scaling factor w, forgenerating the approximation of the noise component, which approximationis then used as the reference input to the adaptive filter to develop areconstruction of the desired signal. However, a significant problemwith the above technique is that by combining the acquired signals togenerate a reference signal that describes the noise component, somepart of the desired component s may be included in the reference signal.Consequently, the desired signal s, or a portion thereof, will beerroneously identified as noise, thereby causing significant errors inreconstruction of the desired signal. The same problem exists if thesystem is operated conversely, where the acquired red and infraredsignals are subtracted from each other after determination of adifferent appropriate scaling factor w, for generating a signalsubstantially similar to the desired signal component s, whichapproximation is then used as the reference input to the adaptivefilter. In the latter case some part of the noise component n may beincluded in the reference signal, thereby also causing significanterrors in reconstruction of the desired signal.

It would be desirable to provide a more reliable manner of reducing thenoise component in an acquired physiological signal.

As will be described next, the present inventor has discovered that inan adaptive cancellation arrangement, to reduce noise and otherartifacts from a desired component of a noisy signal, when the basicstructure of the desired signal is known, knowledge of the basicstructure can be used to improve the operation of the adaptivecancellation arrangement.

SUMMARY OF THE INVENTION

In accordance with the principles of the present invention, an acquiredphysiological signal which may be contaminated with noise, comprising adesired signal component and an undesired noise component, is applied toone input of an adaptive filter, and a synthetic reference signal thatis modeled so as to exhibit a correlation with the desired signalcomponent is applied to another input of the adaptive filter.Thereafter, in a feedback manner, the adaptive filter iterativelyadjusts the synthetic reference signal so as to progressively generateas an output of the filter a more accurate approximation of the desiredsignal component, which approximation becomes a reconstruction of theacquired physiological signal wherein the level of the undesired noisecomponents are reduced. As a result of the invention a more accurate andreliable reconstruction of the physiological signal is developed.

In accordance with a further aspect of the invention, asynchronouslywith the operation of the adaptive filter arrangement, the acquiredphysiological signal is processed so as to identify signal sectionsthereof having a high confidence of being free of undesired noisecomponents, i.e., representative of only the desired signal component.These high confidence sections are then analyzed to generate additionalknowledge about the desired signal component, which knowledge isperiodically used to adjust the synthetic reference signal so that amore accurate representation of the desired signal component of theacquired physiological signal is applied as a reference signal to theadaptive filter arrangement.

In one embodiment of the invention having first and second pulseoximetry signals as the acquired signals, the pulse oximetry signals arecompared with each other, as well as past segments of themselves andeach other, so as to generate the high confidence signal sections. Thesesections are then processed so as to develop phase and frequencyinformation about the desired signal component, which is used to modifythe phase and frequency components of the synthetic reference signal. Inaddition, the high confidence signal sections themselves can be used todirectly modify the shape of the modeled synthetic reference signal.

In an alternative embodiment of the invention a different type ofphysiological signal, such as an EKG signal, may be processed so as todevelop one or more types of adjustment inputs for the modeled syntheticreference signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates in block diagram form the operation of a pulseoximeter in accordance with the principles of the invention.

FIG. 2 illustrates in block diagram form a portion of the operation ofthe processor shown in FIG. 1.

FIG. 3 illustrates in block diagram form the remainder portion of theoperation of the processor shown in FIG. 1.

FIG. 4 illustrates the synthetic reference signal used in the processorshown in FIG. 1.

DETAILED DESCRIPTION

In the following detailed description, an exemplary embodimentcomprising a pulse oximeter is described, however, it is noted that thetechniques of the present invention are broadly applicable to reducingthe noise and artifact components in other types of physiologicalsignals, such as EKG signals, as well as other types acquired signals.Referring to FIG. 1, a physiological monitor 2 is shown having a sensorarrangement 4 comprising, as well known, red 6 and infrared 8 LED's anda photodiode detector 10 arranged in an opposed spatial relationship ina housing, not specifically shown, which is attached to a well perfusedpart of the patient, such as a finger 12 or alternatively on an ear. Atimer 14 causes LED's 6 and 8 to operate in a pulse manner (say at 400Hz), as well known, so as to cause photodiode detector 10 to develop atits output red and infrared oximetry electrical signals in a timedivision multiplexed manner. A demultiplexer 16 of conventional design,is responsive to timer 14 and the time division multiplexed signals, fordeveloping analog red and infrared oximetry electric signals 18 and 20,respectively. An analog to digital converter 22 develops digitized (sayat 100 Hz) red and infrared oximetry electrical signals 24 and 26. Aspreviously described, the acquired oximetry signals are prone torelatively severe noise contamination, thereby hindering their use asreliable indicators of blood oxygenation. Consequently, the digitizedoximetry signals 24 and 26 are provided to a processor 28 for noisereduction processing in accordance with the principles of the presentinvention, for developing at the output of processor 28 noise reduceddigital red and infrared oximetry signals 30 and 32. In accordance withconventional knowledge, the nominal ratio of successive samples of thered and infrared signals is calculated in a saturation processor 34 andconverted to a saturation estimate. Saturation processor 34 provides atits output a series of estimated values 36 for the blood oxygensaturation of the patient, which are then used as the current bloodoxygen saturation estimate output of monitor 2, by use of a display,recorder or transmitter, not specifically shown.

As previously noted, the acquired physiological signals include adesired pulsatile component that is easily contaminated with noisesignals, i.e., artifact, which disturbs the above-noted ratioprocessing, resulting in an inaccurate saturation estimate, and even thegeneration of false alarms by monitor 2. The exact effect of theartifact upon the saturation estimate depends on the specific processingused to develop the estimate.

One way to eliminate the saturation estimate error is by an attempt tocompletely separate the desired pulsatile signal from the artifact. Acentral difficulty with applying the forenoted existing noisecancellation approaches to solve this problem, results from the factthat there is no clean reference signal available to use in an adaptivefilter, and that the artifact cannot be adequately characterized in astatistical sense. The present inventor has realized that the onlyreasonably knowable information about the reference signal, lies in thefact that the desired pulsatile signal derives from a process which canbe reasonably characterized.

Thus, in accordance with the present invention, processor 28 generates asynthetic reference signal that exhibits a correlation with the desiredpulsatile signal, and uses the synthetic reference signal to train anadaptive filter to track the desired pulsatile signal component in theacquired contaminated oximetry signals 24 and 26.

In accordance with a first aspect of the invention, two major parametersare used to calculate the synthetic reference signal, i.e., thesynthetic pulsatile heart rate signal, given a canonical wave-shape.These parameters are the phase and the frequency of the desired heartrate signal.

In accordance with a second aspect of the invention, estimation of thephase and frequency of the desired heart rate signal is performed bysuitable processing of the contaminated oximetry signals. In analternative embodiment of the invention, estimation of the phase and thefrequency of the desired heart rate signal can be performed by suitableprocessing of an additionally acquired further physiological signal,such as an ECG signal. Such suitable processing for an ECG signal iswell know by those of ordinary skill in this technology.

Processing in accordance with the invention can be divided into twoparts: section I, estimation, and section II, filtering. The estimationsection extracts heart rate information from relatively low-noiseregions of digitized lead signals 24 and 26 to help generate a moreaccurate synthetic reference signal. This section processesasynchronously to the filtering section. The filtering section uses thesynthetic reference signal to continuously filter noise contaminatedsignals 24 and 26 to calculate the saturation estimate values.

The major stages of section I of the signal processing are as follows:

1. Signal conditioning

a. Baseband filtering

b. Baseline removal

2. Outlier detection and confidence tagging

a. Amplitude outlier detection

b. Interlead correlation detection

c. Morphological error sequence processing

3. Heart rate estimation, and

4. Synthetic Signal Generation

The major stages of section II of the signal processing are as follows:

5. Adaptive filtering

6. Subspace projection and saturation calculation

7. Saturation estimate filtering

Signal Conditioning

Reference is now made to FIG. 2 which illustrates the above signalprocessing steps. Initial signal conditioning comprises a logarithmictransformation stage 202, a baseband filtering stage 204, and a linearspline baseline removal stage 206.

At stage 202, the logarithm of each sample s n! of digital signals 24and 26 is taken, for introducing linearity properties to theintensity-based (i.e., exponential) signals 24 and 26, therebydeveloping corresponding lead signals 208 and 210, respectively. Signals208 and 210 are then filtered using a Butterworth low pass filter 204having an upper cutoff frequency of 5 Hz, corresponding to 300 bpm, forproducing signals 212 and 214 which are applied to stage 206 for removalof the baseline portion of signals 212 and 214, as next described.

Proper operation of the pulse oximeter depends on the ability tonormalize the acquired signals with respect to the DC light level. Incurrent systems, the present inventor observed that even under whatwould be considered clinically optimal conditions, there is asignificant amount of baseline wander. Therefore, it is not obvious howto normalize the received signal.

Under ideal circumstances, the normalization of the base signal (up to asmall DC offset), is equivalent to high-pass filtering of the signalwith a very high-order filter. This interpretation suggests that thebaseline wander can be removed by high-pass filtering. However,oximeters should be capable of processing heart rates over a very largerange, such as from 15 bpm (beats per minute) to 300 bpm. Carefulspectral analysis of acquired oximetry signals show that the spectrum ofthe baseline wander signal overlaps the spectrum of the pulsatilewaveform. Since insufficient statistical information about the relevantspectra are available, standard techniques (e.g. Wiener filtering),cannot be used to design a linear filter that, in general, can removethe baseline wander.

In accordance with a further aspect of the present invention, in apreferred embodiment of the invention, baseline removal is accomplishedusing a delay buffer of length M (say 500 samples) to buffer datasamples s n! of each of signals 212 and 214. Each incoming signal sampleis evaluated to check whether it is a unique maximum in a window oflength L (say 30 samples). If it is the unique maximum in such a region,it is tagged as being so, and a linear fit consisting of an estimate ofthe offset and of the slope is calculated, based on the value and timeindex of the previously obtained maximum value and the new time indexand local maximum value. The linear fit is used to calculate a baselineestimate for all the samples with time indices between the two localmaxima and then the baseline estimate is subtracted from the signal,thereby generating fully conditioned red and infrared digital signals216 and 218, respectively. If no new local maxima have been found andthe buffer overflows, the current offset and slope estimate is used tofilter the data until valid maxima are obtained.

While it would appear that this procedure is sensitive to spike noise,during tests the above baseline removal procedure performedsignificantly better than a baseline removal stage comprising a linearIIR filter.

Outlier Detection and Confidence Tagging

The next step in section I of the signal processing is outlier detectionand confidence tagging. This is accomplished by stage 220 shown in FIG.2. Stage 220 comprises outlier detection stages 222, a correlationdetector stage 224 and a morphological filter stage 226, that results inthe generation at the output of stage 220 of a stream of final errorcodes 228 that tag (identify) sections in lead signals 216 and 218 thathave a high confidence level of being relatively noise-free. The majorpurpose of stage 220 is to learn gross statistics about each of leadsignals 216 and 218, and to associate with each signal sample s n! ofthese signals, an associated error code, τ n!, which indicates whetherthat signal sample is considered highly contaminated by noise. In stage220, each of a plurality of statistical tests generates a specific errorcode, so that multiple error codes become associated with each signalsample. The resulting error codes are then taken into account in thesubsequent processing stages, as will be described next.

As noted above, one of the statistical tests of stage 220 comprisesinterlead correlation detection. As previously noted, the red andinfrared LED's are operated in a pulsed manner, causing thecorrespondingly acquired optical oximetry signals to be time-divisionmultiplexed. Due to the close spatial proximity of the LED's and therelatively fast multiplexing (400 Hz switching), and a 100 Hz signaldigitization for each signal lead, the absorption representativecomponents of digital signals 216 and 218 change in a highly correlatedway. Accordingly, in the preferred embodiment of the invention, thecorrelation detector stage 224 correlates sections of window length M(say 200 samples), centered around each sample s n! of the digitalsignals 216 and 218. If the correlation coefficient between the twosections of signal samples is less than a vigilance parameter ρ_(corr)(say 0.98), then those signal samples s n! are tagged as invalid basedon insufficient correlation, and a corresponding error code signal 230is provided.

Particular motion artifacts, especially evident in neonates, are suddenlarge spikes and swings in the baseline amplitude level, which areorders of magnitude larger than the nominal signal value. Accordingly,the second statistical test of stage 220, performed by amplitude outlierdetection stages 222, is used to detect these regions in signals 216 and218. Amplitude outlier detection has to have the capability of beingrobust against spike outliers of almost arbitrary magnitude relative tothe nominal size of the AC component.

A simple linear filter (such as a mean filter), would fail to detectthese regions since the spikes contain large amounts of energy whichwould skew the mean towards the outlier regions. Furthermore, since A/Dconverter 22 is not connected in a feed-back loop, the size of the ACcomponent of the acquired signals can not be determined with reasonableaccuracy. Consequently, based on skin to probe pressure, skinpigmentation and perfusion level, the magnitude of the AC component ofthe acquired signals is subject to large changes. The purpose ofamplitude outlier detection is therefore two-fold:

1. To establish an estimate of the absolute magnitude and swing of theAC signal component of the acquired signals, and

2. To establish a detection mechanism to determine with high probabilitywhen excessive signal swings are present.

In accordance with a further aspect of the invention, the mechanism thatis used to adaptively characterize the expected signal swing magnitude,is to maintain a histogram of the swings of each of signals 216 and 218over a large time window. From each such histogram a CumulativeDistribution Function (CDF) is calculated. From the CDF the mediansignal value is calculated, which is a robust statistic from which anestimate of the nominal signal value for each signal is generated, aswell as an outlier detection amplitude threshold. In the preferredembodiment, the nominal signal amplitude has a ratio G relative to themedian, and a detection threshold is set at a value larger than thisnominal signal value for each of signals 216 and 218.

Each histogram is calculated by maintaining a buffer of signal samplesover a large time period up to the present (say 3000 samples), andupdating the histogram periodically (say every 500 samples). On startup,the buffer can be initialized either by using a default expected signalswing (such as can be calculated by standard means from a database ofclean reference data files that are decomposed into AC and DC componentsusing a high pass filter having a lower corner frequency of 30 bpm), orby calculating an initial CDF using some small set (such as the first500) samples obtained by outlier detectors 222 after the monitor isfirst switched on.

To calculate an estimate of the ratio G of the nominal signal amplitudeexpected relative to the median amplitude of the signal, the presentinventor obtained artifact-free reference data from six patients, andthe CDF of the magnitude of the reference signals was calculated. The 95percentile value (η95) and the median (η50) value was found from the CDFfor each of these patients. The 95 percentile value and the median valuewere then plotted against one anther, for the case when spline baselinesignal removal in accordance with stage 206 was used. From these plots,the inventor realized that:

1. The ratio of the η50 value to the η95 value is approximately constantacross the reference signal database, and

2. The ratio of the η95 value to the η50 value is approximately 2.228.

Using the ratio value of 2.228 as an estimate of G, an outlier detectionamplitude threshold is then set conservatively at an amplitude valuelarger than (typically some multiple such as twice) the value ofη50×2.228, measured in log(A/D) units. Setting the detection thresholdin this manner ensures that an overwhelming fraction, typically morethan 95%, of the data samples of a clean signal will be validated byamplitude outlier detection stages 222.

In the illustrated embodiment, a separate amplitude outlier detectorstage 222 processes each of signal leads 216 and 218, and their outputsare combined on a logical OR manner to provide an error code signal 232having a sequence of error codes that indicate an error conditionwhenever a corresponding portion of at least one of signals 216 or 218has an amplitude swing that exceeds the previously determined detectionthreshold.

At the input of morphological filter 226, error code streams 230 and 232provided from correlation detector stage 224 and the outlier detectorstages 222, respectively, are combined using a simple logical (OR) rule,thereby indicating whether the corresponding data samples s n! insignals 216 or 218 exceed the upper limit set for the amplitudemagnitude, and/or whether the interlead correlation vigilance has beenexceeded.

In order to ensure that no small, isolated regions, of signal data aretagged to be invalid and, furthermore, that no small, isolated regions,are tagged as valid, since such small isolated regions are likely to betagged inappropriately, the morphological filter stage 226 performserosion and dilation processing of the OR-ed error code stream.

In a first portion of filter 226, the error code stream is eroded toensure removal of contiguous code sequences where the data samples aretagged as valid but which sequences have a length less than somespecified number M (such as 20) of data samples. This procedure reducesthe chance that isolated samples of low-confidence data which occurs inhigh-noise regions are inappropriately identified as being valid. In asecond stage of filter 226, the thus eroded signal is then dilated,i.e., the error code is changed to indicate valid in contiguous blocksof error codes which indicate corresponding data samples that are taggedto be invalid but where the code sequence length is less than L (say 10)data samples. This ensures that small sections of signal sample datatagged to be invalid but which occur in regions where most of the signalsamples are tagged to be valid, are not inappropriately removed.Finally, the thus modified error code stream is eroded by removing K(such as 3) samples from each end of every error code sample sequencewhich indicates corresponding data samples that are tagged to be valid,since such error codes occur where there is a change from ahigh-confidence region to a low-confidence region, and at thesetransitions data samples are more likely to be corrupted. As aconsequence of the above morphological processing, the final error codestream 228 comprises contiguous blocks of some minimal size of the sameerror value, whereby the high-confidence regions of signals 216 and 218can be clearly identified.

Heart Rate Estimation

Any reliable estimate of the heart rate, such as can be obtained byprocessing of an ECG signal, if available, can be used. However, in theillustrated embodiment of an oximeter, an ECG signal is not available.Consequently, the above described processing of FIG. 2 is used toidentify the high-confidence regions of signals 216 and 218, and anestimate of the patient's heart rate is then calculated directly fromthese regions. As shown in FIG. 3, an FFT estimator 302 is used forcalculation of the heart rate estimate. FFT estimator 302 is responsiveto the error code sequence 228 and one of the digitized lead signals, inthe illustrated embodiment lead signal 216, so that the FFT processingtakes into account only the high-confidence regions of signal 216.Accordingly, only the data samples which are tagged as valid are used inthe FFT calculation, and the rest of the data samples are set equal tozero. A window of M data samples is used by FFT estimator 302, whichsamples are placed in a buffer having a length L≧M, where the buffer isfurther filled in with zero values (i.e., zero-padded) when L>M. Thenumber of samples M used, determines the step response of estimator 302,i.e. how fast the FFT can detect a change in heart rate of the patient.The step response for a distinct change in frequency which is detectablebased on the resolution of the FFT, corresponds to a delay of M/2. Thesize of the FFT buffer L determines the resolution of the calculatedheart rate estimate. For a sampling frequency f_(s), the frequencyresolution (in Hz) is given by:

    Δƒ=ƒ.sub.s /L

After the FFT is calculated, the spectrum corresponding to signals belowthe lower and above the upper detectable heartrates, hr_(min) andhr_(max) respectively, are zeroed out. The power of the FFT is thencalculated, and the primary peak index k_(max) is determined. A heartrate estimate 304 is provided at an output of estimator 302, which isgiven by

    ƒ.sub.hr =k.sub.max /Lƒ.sub.s

Furthermore, the fraction of the M data samples which are tagged asvalid is kept track of in the system. If the fraction is above a presetthreshold ρ_(FFT), the new heart rate is accepted; if the fraction fallsbelow the threshold, indicating very noisy data samples, the old heartrate estimate is maintained. The FFT output estimates 304 are updatedevery K seconds.

The following values were used in tests of the invention:

    ______________________________________    Parameter        Symbol    Value    ______________________________________    FFT Length       L         8192    FFT Window       M         1500 = 15 s    FFT Update Rate  K         500 samples = 5 s    Sample Rate      ƒ.sub.s                               100 Hz    Vigilance Parameter                     ρ     0.7    Heart Rate Resolution                     Δƒ                               0.732 bpm    ______________________________________

Reference Signal Generation

Still referring to FIG. 3, a reference signal generator 306 generates asynthetic pulsatile reference signal 308, that models the arterial pulsecomponent of the acquired lead signals, and from which an approximatebasis for the subspace spanned by the arterial pulse component can begenerated. The received signals 216 and 218 are projected by an adaptivefilter 310 onto the subspace and noise is reduced by this operation. Inaccordance with the preferred embodiment of the invention, the basis isgenerated by a set of frequencies corresponding to the spectrum of thesynthetic reference signal. The fact that a synthetic basis is usedimplies that an approximation error could exist between the basisspanned by the synthetic signal subspace and the basis spanned by thetrue pulsatile heart signal. The technique of the present inventiondepends only on the existence of a component of the true pulsatileabsorption signal being in the subspace of the synthetic pulsatilereference signal, since the adaptive filter can build up appropriatekernels using simple operations on the reference signal. Since largeartifact noise frequently appears at low frequencies, even afterbaseline removal, it is advisable to choose the synthetic referencesignal to have a zero mean, thereby reducing spectral overlap.

Referring to FIG. 4, the generation of the synthetic reference signal isperformed by tracing out a kernel function 402 representative of thepulsatile component for a single cardiac cycle, at a rate equal to theheart rate. Kernel function 402 is the reasonably-knowable part of thedesired pulsatile signal, and is generated so as to have a significantcorrelation to the desired pulsatile component of the acquired signals.The overall shape of the kernel derives from the physiological operationof the cardiopulmonary system, as has been well studied and is describedin, for example, Chapter 2 (Heart Sounds and Murmurs) of the book"Physiology of Heart Disease" ed. L. S. Lilly. As described in thisreference, the cardiac cycle produces a pressure waveform which isroughly triangular, with a fast increase in pressure as the leftventricle contracts, and a slower decay in pressure after the aorticvalve closes. In the default implementation, a beta kernel was usedwhich has a similar behavior of a fast rise time and slow decay, whendefined by

    ψ(t;x,y)=t.sup.x (l-t).sup.y -u.sub.x

where x=0.5, y=1.0, and mu₋₋ x ensures zero mean and has a value ofapproximately 0.2663936. The frequency content of this kernel overlapsthat of most expected patient cardiac cycle waveforms, and biases thekernel which is finally produced by the adaptive filter towardsphysically realizable waveforms.

In the preferred embodiment of the invention, reference signal 308 isgenerated using kernel 402, as defined by:

    ƒ(t)=ψ((θ(t)mod 1)

where the instantaneous phase θ(t) is defined by

    θ(t)=ƒ.sub.hr (t)(t-φ(t))

where ƒ_(hr) (t) is the instantaneous heart rate and φ(t) is a phaseterm. The phase term is required to ensure continuity of the signaloutput when the heart rate frequency estimate changes discontinuously.In particular, if at time to, the heart rate changes from ƒ_(hr) ⁻ toƒ_(hr) ⁺ the phase estimate φ⁺ is given by the convex combination##EQU2##

In summary, the reference signal is generated by repeating the kernelfunction at a rate equal to the current estimate of the heart rate, andwith an appropriate phase chosen to prevent discontinuities in the dataat the point in time when the heart rate last changed.

Adaptive Filtering

Referring again to FIG. 3, an adaptive filter 310 is shown, responsiveto signals 216 and 218 (the digitized versions of the noise contaminatedacquired signals 18 and 20 of FIG. 1), and the synthetic referencesignal 308. As is conventional in adaptive filter operation, internal toadaptive filter 310, synthetic reference signal 308 is graduallyadjusted so that it becomes a more accurate approximation of the appliedreference signal, i.e., the desired pulsatile component of each ofsignals 216 and 218. FIG. 3 conceptually illustrates a preferredimplementation (due to its high numerical efficiency) of an appropriateadaptive filter 310, known as a joint process estimator. The theory andimplementation of such a filter is well known, such as described inChapter 15 of the book titled ADAPTIVE FILTER THEORY, by Simon Haykinpublished by Prentice Hall of Upper Saddle River, N.J.

Briefly, adaptive filter 310 can be separated into several mainprocessing blocks. In a first processing stage 312, the appliedreference signal, μ, is shifted and weighted by a multistage latticeprediction filter (Haykin FIG. 15.6), which generates a new set oforthogonal signals from μ, namely backward prediction errors 313 thatspan the same subspace as the reference signal and M of its versions, byadapting a set of coefficients using well known equations, as alsodescribed by Haykin.

For each signal to be filtered, namely the noise contaminated red andinfrared lead signals 216 and 218, a respective regressor stage 314 and316 is defined. Each regressor stage takes as its input the backwardprediction errors 313 generated by the multistage lattice predictionfilter 312, and the signal to be filtered, denoted d in Haykin FIG.15.7, but in our case, a respective one of the noise contaminatedsignals 216 and 218. Each regressor stage separates its noisecontaminated input signal into a component S_(ref) which can be built upusing linear shifting and weighting of the reference signal, and acomponent S_(noref) which can not be built up from the reference signal.As a result, from the output of regressor stages 314 and 316, areconstruction of just the desired pulsatile component of the acquiredphysiological lead signals is generated as the red and infrared leadsignals 30 and 32, respectively.

Saturation Estimate Calculation and Saturation Estimate Post Processing

As previously described in conjunction with FIG. 1, once noise reducedversions of the acquired oximeter signals are developed, they can beprocessed using conventional ratio processing to develop a blood oxygensaturation estimate. In accordance with the a further aspect of thepresent invention, the conventional ratio calculation processing isimproved. More specifically, as shown in FIG. 3, the above describedprocessing resulted in two sampled oximeter signals, namely 30 and 32,which have been projected into a subspace spanned by a feasible pulsewaveform, as defined by the synthetic pulse signal 308. However, ingeneral, the two signals 30 and 32 are not linearly dependent.Therefore, the signals cannot be divided point by point, in accordancewith the conventional ratio technique, to obtain a constant saturationestimate.

Thus, in accordance with this further aspect of the invention, a ratiocalculator 318 of FIG. 3, projects the thus noise reduced signals 30 and32 onto a common one-dimensional subspace, τ. Selection of thissubspace, τ, can be performed using a minimum disturbance principlewhich states that the angle between the signal vectors is dividedequally. Therefore, the saturation ratio, calculated by ##EQU3## reducesto calculating the norm of the red and infrared signals individuallyover the window of interest (say 201 samples) in NORM processing stage320, for producing norm signals 322 and 324, respectively. These normsignals are then divided by a RATIO processing stage 326 for generatinga sequence of saturation ratio estimates 328. The saturation ratioestimates 328 are converted into blood oxygen saturation estimates forthe patient by applying them as addresses to a look up table (LUT) 330,in the conventional manner. This procedure results in a sequence S n! ofsaturation estimates 332 being provided by LUT 330.

The final stage of the processing shown in FIG. 3 comprises a filter 334for processing the sequence of saturation estimates 332 to even furtherreduce noise and to stabilize the saturation estimates by providing thefiltered median of saturation estimates 332 as the final outputestimate. In accordance with this aspect of the invention, the sequenceof saturation estimates 332 are processed using a two stage filterapproach, not specifically shown. In a first filter stage, a medianfilter is used to remove spikes and outlier values. In a second stage, anonlinear predictor-corrector filter is used to ensure that the medianfilter output is integrated and ensures a maximum rate of change.

The median filter maintains a sliding window containing M saturationestimates obtained from earlier stages of the processing, namely, S n!,S n-1 !. . . S n-M+1 !. Every L seconds, the array is sorted. The top Pand bottom P ranked elements are discarded, and the remainder of thedata is averaged to obtain a value which is used as a median estimateS_(m) n! for the next L seconds.

This output of the median filter is subsequently filtered using thefollowing nonlinear predictor-corrector filter:

    x n!=(1+60)x n-1!+αx n-2!

    x n!=x n!+β tan h(S.sub.m  n!-x n!)

In our algorithms, we used the following parameters:

    ______________________________________    Parameter         Symbol    Value    ______________________________________    Median filter window                      M         1000 samples = 10 s    Median filter upper percentile                      P         300 samples = 30%    Median filter update frequency                      L         100 samples = 1 s    Prediction momentum                      α   0.9    Corrector magnitude                      β    1e-4    ______________________________________

Thus, what has been shown and described is a novel method and apparatusfor reducing noise signals in a physiological signal in a manner whichfulfills all the advantages and objects sought therefore. Many changes,modifications, variations and other uses and applications of the subjectinvention will however be apparent to those of ordinary skill in the artafter consideration of this specification and its accompanying drawings,which disclose a preferred embodiment thereof. For example, although inthe illustrated embodiment noise is reduced in an oximetry signal, theinvention can find applicability for reducing noise in any physiologicalsignal having a pulsatile component. In this regard, although theinvention is illustrated in an oximeter, the monitor may in factcomprise a multiparameter monitor that monitors blood oxygen saturationas well as ECG signals. In this case, the ECG signals themselves can beused to determine an estimate of the heart rate value, or alternatively,the heart rate value can be determined by means other than by use of anECG signal, and for example, by use of an ultrasound, accelerometer,nuclear magnetic resonance, electrical impedance, or other signalacquiring technique.

Furthermore, in the illustrated embodiment the synthetic referencesignal is generated so as to be piecewise constant in frequency, and theshape of the synthetic kernel is invariant and fixed beforehand. In analternative embodiment, straightforward modifications can be made wherethe kernel function is segmented from the high-confidence sections ofthe lead signal.

Additionally, when using the adaptive filter technique described in thepreferred embodiment, alternative techniques can be used to measure thenoise levels in the acquired physiological signals, and the sensitivityor weighting given to the saturation and signal estimates in the varioussignal sections can be adjusted based on the noise estimates. Examplesof such noise estimates are the existing error codes 228, the fractionof the signal power filtered out by the adaptive filter, or the width ofthe FFT spectral peak.

Means could be provided to develop more reasonable initializationsettings. That is, the adaptive filter and outlier detector performancecould be sub-optimal during the first 20 to 30 seconds of operation,since not enough data is available in such a short time to get goodestimates of the signal statistics. An existing heart rate estimatorwhich can produce a heart rate estimate in a shorter period of time canbe used for reference generation.

Finally, the statistics generated by the outlier detection system can beused to further filter the incoming lead signals, for example, byclipping excessively large sample values at some secondary thresholdvalue before inputting these signals to the adaptive filter.

In view of the above, the scope of the invention is intended to belimited only by the following claims.

What I claim is:
 1. A method for reducing the level of an undesirednoise component in an acquired signal having a desired signal component,comprising the following steps:acquiring a signal; generating asynthetic reference signal that is modeled to exhibit a correlation withthe desired signal component of the acquired signal; applying to oneinput of an adaptive filter arrangement said acquired signal, and toanother input of said adaptive filter arrangement said syntheticreference signal; and operating said adaptive filter arrangement in afeedback manner, so that the adaptive filter arrangement iterativelyadjusts the applied synthetic reference signal so as to progressivelygenerate as an output of the filter arrangement a more accurateapproximation of the desired signal component of the acquired signal. 2.The method of claim 1, wherein said generating step comprises:generatingsaid reference signal by processing a predetermined kernel function thatis representative of a pulsatile cycle of the desired signal componentof the acquired signal.
 3. The method of claim 1, wherein the operatingstep of said adaptive filter comprises:shifting and weighting of saidsynthetic reference signal using a multistage lattice prediction filterto generate backward prediction error signals; and combining saidacquired signal with shifted and weighted version of said backwardprediction error signals, to iteratively build-up a reconstruction ofthe desired component of said acquired signal.
 4. The method of claim 1,including a further step of:confidence processing of the acquired signalso as to identify sections thereof having a high confidence of beingrelatively noise-free.
 5. The method of claim 4, wherein said acquiringstep acquires a physiological signal from a patient.
 6. The method ofclaim 5, including a further step of:processing of said high confidencesections of the acquired physiological signal to develop values that areused to adjust the generation of the synthetic reference signal so thatit exhibits a greater correlation with the desired signal component ofthe acquired physiological signal.
 7. The method of claim 6,wherein:said acquiring step comprises acquiring first and second ones ofacquired physiological signals; and said confidence processing stepcomprises subjecting the first and second ones of said acquiredphysiological signals to a correlation test, and then identifyingsections of each of said acquired physiological signals that exhibit acorrelation that exceeds a predetermined threshold level as beingrelatively noise-free, and identifying sections of each of said acquiredphysiological signals that exhibit a correlation that does not exceedthe predetermined threshold level as being relatively noisy.
 8. Themethod of claim 6, wherein said processing of said high confidencesections of said acquired physiological comprises FFT processing of saidhigh confidence sections to develop a value representative of at least afundamental frequency component of the desired signal component of theacquired physiological signal, which value is used to adjust themodeling of the synthetic reference signal so as to increase itscorrelation with the desired signal component of the acquiredphysiological signal.
 9. The method of claim 6, wherein said confidenceprocessing comprises:subjecting said acquired physiological signal to anamplitude threshold test; and identifying sections of said acquiredphysiological signal that have an amplitude that does not exceed apredetermined amplitude threshold level as being relatively noise-free,and identifying sections of said acquired physiological signal that havean amplitude that does exceed the predetermined amplitude thresholdlevel as being relatively noisy.
 10. The method of claim 9, wherein:saidacquiring step comprises acquiring first and second ones of acquiredphysiological signals; and said confidence processing step comprisessubjecting the first and second ones of said acquired physiologicalsignals to a correlation test, and then identifying sections of each ofsaid acquired physiological signals that exhibit a correlation thatexceeds a predetermined threshold level as being relatively noise-free,and identifying sections of each of said acquired physiological signalsthat exhibit a correlation that does not exceed the predeterminedthreshold level as being relatively noisy.
 11. The method of claim 10,wherein said confidence processing step comprises the further stepsof:developing an error code stream comprising a sequence of error codesthat identify corresponding successive sections of said acquiredphysiological signals that have been identified as being eitherrelatively noise-free or noisy; and morphological processing of saiderror code stream to increase the degree of confidence that sections ofsaid acquired physiological signal identified as being relativelynoise-free or relatively noisy have been correctly identified.
 12. Themethod of claim 11, wherein said morphological processing stepcomprises:erosion of said error code stream, by changing a sequence ofcontiguous error codes in said error code stream to identify as noisythose of said error codes previously identified as relatively noise-freebut having a sequence length that is less than a first predeterminedlength M, and dilation of said error code stream, by changing a sequenceof contiguous error codes in said error code stream to identify asrelatively noise-free those of said error codes previously identified asnoisy but having a sequence length that is less than a secondpredetermined length L.
 13. The method of claim 12, wherein said erosionstep sets said length M to be greater than said length L.
 14. Apparatusfor reducing the level of an undesired noise component in an acquiredphysiological signal having a desired signal component, comprising:asensor arrangement for acquiring a physiological signal; a signalgenerator for modeling a synthetic reference signal that exhibits acorrelation with the desired signal component of the acquiredphysiological signal; and an adaptive filter arrangement having a firstinput responsive to said acquired physiological signal, and a secondinput responsive to said synthetic reference signal, said adaptivefilter arrangement being operated in a feedback manner so as toiteratively adjust the modeled synthetic reference signal andprogressively generate as an output of the filter arrangement a moreaccurate approximation of the desired signal component.
 15. Theapparatus of claim 14, wherein said signal generator comprises:aprocessor that is responsive to a predetermined kernel functionrepresentative of a pulsatile cycle of the desired signal component ofthe acquired physiological signal.
 16. The apparatus of claim 14,wherein said adaptive filter comprises:a multistage lattice predictionfilter for shifting and weighting of said synthetic reference signal togenerate backward prediction error signals; and a regressor processingstage for iteratively combining said acquired physiological signal withshifted and weighted versions of said backward prediction error signals,to progressively build-up a reconstruction of the desired component ofsaid acquired physiological signal.
 17. The apparatus of claim 14,including:a confidence processor for processing the acquiredphysiological signal so as to identify sections thereof having a highconfidence of being relatively noise-free.
 18. The apparatus of claim17, further including:an extraction processor responsive to the acquiredphysiological signal and to the confidence processor, so as to onlyprocess said high confidence sections of the acquired physiologicalsignal, and in response to said processing develop a value that is usedto adjust the generation of the synthetic reference signal so that itexhibits a greater correlation with the desired signal component of theacquired physiological signal.
 19. The apparatus of claim 18,wherein:said sensor arrangement acquires first and second ones ofacquired physiological signals; and said confidence processorcomprises:a correlation detector for subjecting the first and secondones of said acquired physiological signals to a correlation test; andan error code tagger responsive to said correlation detector foridentifying sections of each of said acquired physiological signals thatexhibit a correlation that exceeds a predetermined threshold level asbeing relatively noise-free, and identifying sections of each of saidacquired physiological signals that exhibit a correlation that does notexceed the predetermined threshold level as being relatively noisy. 20.The apparatus of claim 18, wherein said extraction processorcomprises:an FFT processor responsive to said confidence processor, forprocessing only those sections of the first and second ones of saidacquired physiological signals that are identified to have a highconfidence of being relatively noise-free, to develop a valuerepresentative of at least a fundamental frequency component of thedesired signal component of the acquired physiological signal, whichvalue is used to adjust the modeling of the synthetic reference signalso as to increase its correlation with the desired signal component ofthe acquired physiological signal.
 21. The apparatus of claim 18,wherein said confidence processor comprises:an amplitude detector forsubjecting said acquired physiological signal to an amplitude thresholdtest; and an error code tagger for identifying sections of said acquiredphysiological signal that have an amplitude that does not exceed apredetermined amplitude threshold level as being relatively noise-free,and identifying sections of said acquired physiological signal that havean amplitude that does exceed the predetermined amplitude thresholdlevel as being relatively noisy.
 22. The apparatus of claim 21,wherein:said sensor arrangement acquires first and second ones ofacquired physiological signals; and said confidence processor includes:acorrelation detector for subjecting the first and second ones of saidacquired physiological signals to a correlation test; and said errorcode tagger is responsive to said correlation detector for developing anerror code stream that identifies sections of each of said acquiredphysiological signals that exhibit a correlation that exceeds apredetermined threshold level as being relatively noise-free, andidentifies sections of each of said acquired physiological signals thatexhibit a correlation that does not exceed the predetermined thresholdlevel as being relatively noisy.
 23. The apparatus of claim 22, whereinsaid extraction processor comprises:an FFT processor responsive to saiderror code stream and the first and second ones of said acquiredphysiological signals, for processing only those sections of the firstand second ones of said acquired physiological signals that areidentified by said error code stream to have a high confidence of beingrelatively noise-free, to develop a value representative of at least afundamental frequency component of the desired signal component of theacquired physiological signals, which value is used to adjust themodeling of the synthetic reference signal so as to increase itscorrelation with the desired signal component of the acquiredphysiological signals.
 24. The apparatus of claim 22, furtherincluding.a morphological processor for processing the error code streamto increase the degree of confidence that sections of said acquiredphysiological signal identified as being relatively noise-free orrelatively noisy have been correctly identified.
 25. The apparatus ofclaim 24, wherein said morphological processor comprises:an eroder foreroding said error code stream, by changing a sequence of contiguouserror codes in said error code stream to identify as noisy those of saiderror codes previously identified as relatively noise-free but having asequence length that is less than a first predetermined length M, and adilator for dilating said error code stream, by changing a sequence ofcontiguous error codes in said error code stream to identify asrelatively noise-free those of said error codes previously identified asnoisy but having a sequence length that is less than a secondpredetermined length L.